@inproceedings{lo-nicolai-2021-linguistic,
title = "Linguistic Knowledge in Multilingual Grapheme-to-Phoneme Conversion",
author = "Lo, Roger Yu-Hsiang and
Nicolai, Garrett",
editor = "Nicolai, Garrett and
Gorman, Kyle and
Cotterell, Ryan",
booktitle = "Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigmorphon-1.15",
doi = "10.18653/v1/2021.sigmorphon-1.15",
pages = "131--140",
abstract = "This paper documents the UBC Linguistics team{'}s approach to the SIGMORPHON 2021 Grapheme-to-Phoneme Shared Task, concentrating on the low-resource setting. Our systems expand the baseline model with simple modifications informed by syllable structure and error analysis. In-depth investigation of test-set predictions shows that our best model rectifies a significant number of mistakes compared to the baseline prediction, besting all other submissions. Our results validate the view that careful error analysis in conjunction with linguistic knowledge can lead to more effective computational modeling.",
}
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%0 Conference Proceedings
%T Linguistic Knowledge in Multilingual Grapheme-to-Phoneme Conversion
%A Lo, Roger Yu-Hsiang
%A Nicolai, Garrett
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F lo-nicolai-2021-linguistic
%X This paper documents the UBC Linguistics team’s approach to the SIGMORPHON 2021 Grapheme-to-Phoneme Shared Task, concentrating on the low-resource setting. Our systems expand the baseline model with simple modifications informed by syllable structure and error analysis. In-depth investigation of test-set predictions shows that our best model rectifies a significant number of mistakes compared to the baseline prediction, besting all other submissions. Our results validate the view that careful error analysis in conjunction with linguistic knowledge can lead to more effective computational modeling.
%R 10.18653/v1/2021.sigmorphon-1.15
%U https://aclanthology.org/2021.sigmorphon-1.15
%U https://doi.org/10.18653/v1/2021.sigmorphon-1.15
%P 131-140
Markdown (Informal)
[Linguistic Knowledge in Multilingual Grapheme-to-Phoneme Conversion](https://aclanthology.org/2021.sigmorphon-1.15) (Lo & Nicolai, SIGMORPHON 2021)
ACL